AIMC Topic: X-Rays

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Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images.

BMC medical imaging
BACKGROUND: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may intro...

Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images.

Medical physics
BACKGROUND: Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT).

Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.

Scientific reports
In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy severely resulting in high casualties and in the near breakdown of primary care. Due to these facts, ...

Deep learning for automatic mandible segmentation on dental panoramic x-ray images.

Biomedical physics & engineering express
Many studies in the last decades have correlated mandible bone structure with systemic diseases like osteoporosis. Mandible segmentation, as well as segmentation of other oral structures, is an essential step in studies that correlate oral structures...

Evaluation of the clinical performance of an AI-based application for the automated analysis of chest X-rays.

Scientific reports
The AI-Rad Companion Chest X-ray (AI-Rad, Siemens Healthineers) is an artificial-intelligence based application for the analysis of chest X-rays. The purpose of the present study is to evaluate the performance of the AI-Rad. In total, 499 radiographs...

Biases associated with database structure for COVID-19 detection in X-ray images.

Scientific reports
Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series ...

Deep Learning Algorithms with LIME and Similarity Distance Analysis on COVID-19 Chest X-ray Dataset.

International journal of environmental research and public health
In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep ...

Unsupervised anomaly detection for posteroanterior chest X-rays using multiresolution patch-based self-supervised learning.

Scientific reports
The demand for anomaly detection, which involves the identification of abnormal samples, has continued to increase in various domains. In particular, with increases in the data volume of medical imaging, the demand for automated screening systems has...

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Journal of biomedical optics
SIGNIFICANCE: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP...

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss.

Computers in biology and medicine
-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary ...